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Deny Zainal Arifin
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matics@uin-malang.ac.id
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+6285646744340
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matics@uin-malang.ac.id
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Jurusan Teknik Informatika Fakultas Sains dan Teknologi Universitas Islam Negeri Maulana Malik Ibrahim Malang Jalan Gajayana 50 Malang, Jawa Timur, Indonesia 65144
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INDONESIA
MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
Published by Universitas Islam Negeri Maulana Malik Ibrahim Malang
ISSN : 1978161X     EISSN : 24772550     DOI : https://doi.org/10.18860/mat
Core Subject : Science,
Computer Science & IT
MATICS is a scientific publication for widespread research and criticism topics in Computer Science and Information Technology. The journal is published twice a year, in March and September by Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia. The journal publishes two regular issues per year in the following areas : Algorithms and Complexity; Architecture and Organization; Computational Science; Discrete Structures; Graphics and Visualization; Human-Computer Interaction; Information Assurance and Security; Information Management; Intelligent Systems; Networking and Communication; Operating Systems; Platform-Based Development; Parallel and Distributed Computing; Programming Languages; Software Development Fundamentals; Software Engineering; Systems Fundamentals; Social Issues and Professional Practice.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 6 Documents
 1 
Search results for , issue "Vol 9, No 2 (2017): MATICS" : 6 Documents clear
Ekstraksi Ciri Sinyal EEG Untuk Gangguan Penyakit Epilepsi Menggunakan Metode Wavelet ANI, WIWIT PUTRI
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v9i2.4376

Abstract

Abstrak- Epilepsy terjadi karena ada gangguan sistem saraf otak pada manusia, yang terekam dari sinyal Elektroensephalogram. Sinyal Elektroensephalogram memiliki informasi aktivitas listrik pada otak, termasuk kondisi gangguan kelistrikan dan pikiran pada syaraf. Sinyal Elektroensephalogram mempiliki bentuk yang kompleks, mudah tertimbun noise , amplitudo kecil dan tidak memiliki pola yang baku, sehingga analisa secara visual tidak mudah[1] Untuk meningkatkan akurasi dan menghilangkan noise dari sinyal EEG, penelitian ini menggunakan metode Wavelet sebagai proses ekstraksi ciri dan Backpropagation untuk klasifikasi. Data sinyal Elektroensephalogram didapat dari Universitas Bonn yang terdiri dari 5 kelas dataset yaitu A, B, C, D, dan E. Tiap dataset berisi 100 segmen EEG saluran tunggal dengan durasi selama 23.6 detik. Peneliti menggunakan dataset B dan E. Pada tahap pelatihan (training) menggunakan 80 naracoba , sedangkan pada tahap pengujian (testing) menggunakan 100 naracoba. Proses ini dilakukan setelah ekstraksi ciri sinyal EEG dengan Wavelet. Hasil ekstraksi ciri digunakan sabagai nilai input, pada penelitian ini menggunakan metode back propagation (16-35-2) yaitu 2 input sinyal EEG,  satu hidden layer dengan 35 unit dan dua target epilepsy dan non epilepsi . dari pengujian data tersebut didapat nilai akurasi sebesar 100%. Kata kunci : Backpropagation, Wavelet, epilepsy, EEG
Pendeteksian Ketidaklengkapan Kebutuhan Dengan Teknik Klasifikasi Pada Dokumen Spesifikasi Kebutuhan Perangkat Lunak Nurfauziah, Suci
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.237 KB) | DOI: 10.18860/mat.v9i2.4291

Abstract

Dokumen Spesifikasi Kebutuhan Perangkat Lunak (SKPL) dihasilkan dari proses rekayasa kebutuhan dan merupakan tahapan yang kritis pada pengembangan perangkat lunak. Kesalahan yang terjadi pada proses rekayasa kebutuhan akan mempengaruhi ketidakberhasilan produk tersebut. Dokumen SKPL sering kali ditulis dengan bahasa alamiah. Salah satu karakteristik spesifikasi kebutuhan yang baik adalah lengkap. Kualitas spesifikasi kebutuhan bisa dinilai berdasarkan pernyataan kebutuhan atau dokumen kebutuhan. Spesifikasi kebutuhan yang lengkap secara jelas mendefinisikan semua situasi yang dihadapi sistem dan dapat dipahami tanpa melibatkan atau terkait pada kebutuhan lain. Penelitian ini bertujuan untuk membangun model klasifikasi pendeteksian ketidaklengkapan kebutuhan pada dokumen spesifikasi kebutuhan perangkat lunak yang ditulis dengan bahasa alamiah. Penelitian ini membuat corpus kebutuhan yang berisi pernyataan kebutuhan lengkap dan pernyataan kebutuhan tidak lengkap. Corpus ditulis secara manual oleh tiga orang ahli. Dari Corpus akan dilakukan ekstraksi fitur, pemilihan fitur yang valid, dan pembangkitan kata kunci.  Nilai performansi Gwet’s AC1 digunakan untuk mengetahui apakah classifier yang dibangun dapat diandalkan dan dapat mendeteksi adanya ketidaklengkapan pada dokumen spesifikasi kebutuhan perangkat lunak.Berdasarkan hasil ujicoba dengan menggunakan kombinasi metode adaboost dan C4.5 diperoleh rata-rata indek kesepakatan pada level moderate dengan nilai tertinggi 0.52 pada saat penggunaan enam fitur teratas. Enam fitur teratas yang paling berpengaruh antara lain bad_jj, bad_rb, jml_kt_penegasan, jml_kt_penghubung, bad_prp dan jml_kt_negatif.
Performance Comparison of Rule Generation Method Substractive Clustering and Fuzzy C-Means Clustering on Sugeno's Inference for Stroke Risk Detection Mardi Putri, Rekyan Regasari; Santoso, Edy
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (598.178 KB) | DOI: 10.18860/mat.v9i2.4587

Abstract

Abstract - Fuzzy Inference is one method that cansolve the problem of uncertainty in a decision-makingor classification well. In inference, fuzzy rules thatrepresent the need of expert knowledge in the relevantfields, so that the classification given decision or beappropriate expert knowledge. However there are timeswhen experts are less able to represent the rules of theappropriate knowledge or knowledge that there is needof too many rules, so we need a method that cangenerate rules based on the data given expert.At issue troke s disease risk detection, it also occursbecause of the research that has been done by taking thedirect rule of experts, it turns out less than the maximumaccuracy, still 82.89%. Substractive methodsClustering and Fuzzy C-Means (FCM) could generaterules by grouping algorithm, in which the existingtraining data are grouped in common and the rules ofthe group raised. Differences in the two methods are indetermining the center of the cluster and assign eachincoming data which groups.Based on research that has been done, substractiveaverage Clustering membrika better accuracy is84.46%, while 73.81% FCM. However, in theprocessing time FCM faster at 16.75 seconds to give anaverage processing time of 13:02 seconds.
Pendukung Keputusan Penentuan Jumlah Order Menggunakan Fuzzy Mamdani Sonalitha, Elta
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (933.029 KB) | DOI: 10.18860/mat.v9i2.4373

Abstract

To get customer satisfaction, a restaurant should always provide raw materials in accordance with the menu. Each raw material has a different demand based on an uncertain customer interest. Purchasing managers have difficulty in determining the number of orders for each raw material, due to the uncertainty of demand and supply. Therefore we built decision support system for determining the number of orders using fuzzy mamdani. From decision support system we get ROP and recommendation of the number of orders accompanied by the total purchase price for each raw material. This system helps the purchasing managers in determining the amount of orders quickly and precisely by considering the losses, especially in the field of financial management.
Eggs Fertilities Detection System on the Image of Kampung Chicken Egg Using Naive Bayes Classifier Algorithm Diantoro, Aris; Santoso, Irwan Budi
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (663.07 KB) | DOI: 10.18860/mat.v9i2.4198

Abstract

Losses in chicken eggs hatchery make breeders income declined. The main cause of these things because it is less effective and efficient in distinguishing the state of fertilities in the eggs. The detection of fertile and infertile eggs will automatically provide ease of selection and removal of the eggs are fertile and infertile eggs. This will bring more profits for breeder as well as time efficiency more and selling power. Infertile eggs will give breeders the sale price if it is known as early as possible in order not to fail hatching. A method fuzzy c means and naive bayes classifier is designed to identify the state of the fertility of eggs. By putting eggs near the source light and black background in a dark room, then taked of image with a high qualities camera. From the resulting camera image, then extracted features or take characteristics that distinguish between fertile and infertile eggs. The total amount of data used in this study of 450 eggs image sourced from the field survey. Training data is used   250 data, 125 fertile eggs image data and 125 infertile eggs image data. As for testing the data using the 200 data, the image data 150 fertile eggs and 50 infertile eggs image data. Based on trial results of training data is obtained the best accuracy is equal to 80% at intervals of 5, 86.4% at intervals of 5 and dimensions 70x60, and 99.6% on 1x2 resize. The accuracy of the results obtained by 78%, 82% and 94% in trials testing data.
Aplikasi Market Matching Berbasis Fuzzy sebagai Penunjang Keputusan Ekspor Produk UMKM Nurdewanto, Bambang
MATICS Vol 9, No 2 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (738.032 KB) | DOI: 10.18860/mat.v9i2.4372

Abstract

Determining the exact location of the export market with the right amount in the marketing process is expected to reduce the number of losses due to the stagnancy of product turnover. Appropriate target market system using fuzzy control on MSMEs. Fuzzy control method is used to overcome the determination of a market that is influenced by the subjectivity of marketing actors. Online market matching application which is the right decision support system of the right export destination and the right amount so efficient. The result of market matching application of fuzzy method is recommendation of destination and quantity that can be exported.

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